US11055630B2 - Multitemporal data analysis - Google Patents
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- US11055630B2 US11055630B2 US15/790,206 US201715790206A US11055630B2 US 11055630 B2 US11055630 B2 US 11055630B2 US 201715790206 A US201715790206 A US 201715790206A US 11055630 B2 US11055630 B2 US 11055630B2
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Definitions
- the disclosure relates to the field of machine learning, particularly to general and decomposable data analysis.
- Data analysis may often be required to be done on massive amounts of data. Even though the data may be labeled in one form or another, the data may not have a uniform format since it may originate from different sources, or the data may contain a lot of irrelevant data which may need to be formalized for maximum analysis efficiency. The data may also contain elements that may be better suited for other means of analysis that is not provided by the current system. Creating a data analysis model from scratch may be daunting, and manually curating such large amounts of data may prove to be a tedious and time-consuming task.
- a serverless application in which a developer does not have to create a backend server infrastructure for their application.
- the developer may user a Platform as a Service (PaaS) solution such as AMAZON LAMBDA to simplify their backend requirements.
- PaaS Platform as a Service
- AMAZON LAMBDA Platform as a Service
- a serverless application may require a system with real-time streaming data-handling capabilities.
- the computer systems used may differ as well, since a system well-suited for analyzing large amounts of data may be not able to analyze real-time streaming data.
- a system in which data may be input into a system by a user.
- the system determines the best course for analyzing the data, which may include, without limitation, mapping and reducing the data, splitting general and decomposable data, and directed computation graph analysis.
- the data that may be put into the system may include, without limitation, large and small amounts of stored data, live streaming data, and the like.
- FIG. 1 is a diagram of an exemplary architecture of a business operating system according to an embodiment of the invention.
- FIG. 2 is a sequence flow diagram summarizing a method for taking data input from a data source to perform analysis and functions with a transformer service as used in various embodiments of the invention.
- FIG. 3 is a flowchart illustrating a method for data input and splitting for multitemporal data analysis used in various embodiments of the invention.
- FIG. 4 is a flowchart illustrating a method for analyzing data using a general transformer service module as used in various embodiments of the invention.
- FIG. 5 is a flowchart illustrating a method for analyzing decomposable data with a decomposable transformer service module as used in various embodiments of the invention.
- FIG. 6 is a block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention.
- FIG. 7 is a block diagram illustrating an exemplary logical architecture for a client device, according to various embodiments of the invention.
- FIG. 8 is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services, according to various embodiments of the invention.
- FIG. 9 is another block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention.
- the inventor has conceived, and reduced to practice, a system and method for multitemporal data analysis.
- Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise.
- devices that are in communication with each other may communicate directly or indirectly through one or more communication means or intermediaries, logical or physical.
- steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step).
- the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the aspects, and does not imply that the illustrated process is preferred.
- steps are generally described once per aspect, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some aspects or some occurrences, or some steps may be executed more than once in a given aspect or occurrence.
- FIG. 1 is a diagram of an exemplary architecture of a business operating system 100 according to an embodiment of the invention.
- Directed computational graph module 155 retrieves one or more streams of data from a plurality of sources, which includes, but is not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information.
- a plurality of sources which includes, but is not limited to, a plurality of physical sensors, network service providers, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information.
- data may be split into two identical streams in a specialized pre-programmed data pipeline 155 a , wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis.
- the data may be then transferred to a general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis.
- Directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph.
- High-volume web crawling module 115 may use multiple server hosted preprogrammed web spiders which, while autonomously configured, may be deployed within a web scraping framework 115 a of which SCRAPYTM is an example, to identify and retrieve data of interest from web based sources that are not well tagged by conventional web crawling technology.
- Multiple dimension time series data store module 120 may receive streaming data from a large plurality of sensors that may be of several different types.
- Multiple dimension time series data store module 120 may also store any time series data encountered by system 100 such as, but not limited to, environmental factors at insured client infrastructure sites, component sensor readings and system logs of some or all insured client equipment, weather and catastrophic event reports for regions an insured client occupies, political communiques and/or news from regions hosting insured client infrastructure and network service information captures (such as, but not limited to, news, capital funding opportunities and financial feeds, and sales, market condition), and service related customer data.
- Multiple dimension time series data store module 120 may accommodate irregular and high-volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data.
- programming wrappers 120 a for languages—examples of which may include, but are not limited to, C++, PERL, PYTHON, and ERLANGTM—allows sophisticated programming logic to be added to default functions of multidimensional time series database 120 without intimate knowledge of the core programming, greatly extending breadth of function.
- Data retrieved by multidimensional time series database 120 and high-volume web crawling module 115 may be further analyzed and transformed into task-optimized results by directed computational graph 155 and associated general transformer service 160 and decomposable transformer service 150 modules.
- graph stack service module 145 represents data in graphical form influenced by any pre-determined scripted modifications 145 a and stores it in a graph-based data store 145 b such as GIRAPHTM or a key-value pair type data store REDISTM, or RIAKTM, among others, any of which are suitable for storing graph-based information.
- Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the data already available in automated planning service module 130 , which also runs powerful information theory-based predictive statistics functions and machine learning algorithms 130 a to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions. Then, using all or most available data, automated planning service module 130 may propose business decisions most likely to result in favorable business outcomes with a usably high level of certainty.
- action outcome simulation module 125 with a discrete event simulator programming module 125 a coupled with an end user-facing observation and state estimation service 140 , which is highly scriptable 140 b as circumstances require and has a game engine 140 a to more realistically stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data.
- FIG. 2 is a sequence flow diagram summarizing a method 200 for taking data input from a data source to perform analysis and functions with a transformer service as used in various embodiments of the invention.
- data is input into a system configured to use business operating system 100 .
- the data may be, for example, pre-gathered data, or it may be data that is being gathered in real-time during analysis.
- the data is queued to a graph stack service module to be converted into directed computational graph (DCG) form.
- DCG directed computational graph
- Other examples of data may include, without limitation, data gathered by business operating system 100 and stored in local or cloud data stores; data gathered, and aggregated in real-time via web crawling; large amounts of; user-generated events caused by their actions in an application or website; and the like.
- the data, now in DCG form, is queued to a DCG service module for graphical analysis. Analysis may include the system determining which transformer service the data should be queued to for a best outcome for analysis.
- the DCG data is determined by the DCG service module to be appropriate for general transformer service 160 at step 215 .
- Some examples of data suitable for the general transformer service may include, without limitation, large batches data, data stored on distributed databases such as RIAK, data that is generally suited for linear operations, data gathered and stored from sensors or monitoring software overtime, or the like.
- step 220 there may be decomposable data elements within the general data that may be extracted by business operating system 100 and queued to decomposable transformer service for further analysis.
- the input data may be determined to contain data suitable for decomposable transformer service module 150 at step 225 .
- the data is queued directly to the decomposable transformer service module.
- Some examples of data suitable for the decomposable transformer service module may include, without limitation, live streaming data received from sensors or monitoring software, events caused from user action on a website or app, non-linear operations, new social media postings, and, without a loss of generality, highly parallelizable tasks that don't share state.
- the real-time data handling capabilities of the decomposable transformer service may be utilized as a maintenance-free backend that may be used for applications, and web development. This may enable a developer to focus on creating their software, and not have to worry building a suitable backend infrastructure and maintaining it.
- the dynamic data analyzing capabilities of this system allows for a multitude of applications for any amount of data. For instance, using the correct model for a particular query the system can handle the data gathering, parsing, and analysis. For example, a data analyst may want to get a sense of what the general public thinks of certain political candidate. The analyst may develop his own model, download a model from a repository, or purchase a model created by another user to use in his system configured to run business operating system 100 . The analyst may configure his system to automate data gathering from social media feeds, news feeds, message board postings, and the like. The analyst's system, using the transformer services described herein along with other functions of business operating system 100 , may integrate the feeds, map and summarize the data, analyze the sentiment from the gathered data using the model, and generate a report based on the results.
- FIG. 3 is a flowchart illustrating a method 300 for data input and splitting for multitemporal data analysis used in various embodiments of the invention.
- data is input into a system configured to run business operating system 100 .
- the data may comprise, for instance, user input, previously gathered data, data that is being gathered on-the-fly in real-time, or the like.
- the data may also be a combination of the multiple types previously mentioned.
- the input data is queued, and filtered by the system to collect the relevant parts of the data.
- the system may split the data, and determine the type of data and the most appropriate module for further analysis depending on degree of shared state information as part of a declarative formalism for message passing between atomic workers (computing instances) in the pool of distributed computing resources. If the data is determined to be appropriate for the general transformer service module, step 320 is reached, wherein the process continues at step 405 in method 400 , which is discussed below. On the other hand, if the data is determined to be appropriate for the decomposable transformer service module, step 325 is reached, wherein the process may continue at step 505 in method 500 , which is also discussed below.
- FIG. 4 is a flowchart illustrating a method 400 for analyzing data using a general transformer service module as used in various embodiments of the invention.
- general data is queued.
- One source of data is discussed in method 300 .
- the data is formalized into an efficient, database-friendly format and stored for processing. Storage may be handled by a distributed database solution such as RIAK.
- the data is broken up and mapped to a metric specified by a user, and the mapped data is summarized based at least in part by the specified metric.
- biases in the data may be determined.
- step 425 Any decomposable elements in the data at split off and queued to the decomposable transformer service module to step 425 , a method in which is discussed below in FIG. 5 .
- step 425 the general data is aggregated and compiled into a report at step 430 .
- the system may perform an action pre-configured by a user. Actions may include, for instance, a program function, sending an alert, activating a trigger, or the like.
- FIG. 5 is a flowchart illustrating a method 500 for analyzing decomposable data with a decomposable transformer service module as used in various embodiments of the invention.
- decomposable data is queued at the decomposable transformer service module.
- the system determines whether the operation should remain in an iterative loop. The loop may terminate, for example, when there is no more data to analyze, when a trigger is activated, when an alert is received, or a pre-specified event has occurred. If no more iterations are required, the cycle terminates, and an action is performed at step 515 .
- actions may include, for instance, a program function, sending an alert, activating a trigger, or the like.
- the system determines whether the model used for analysis should be retrained with the iterative data at step 520 . If the system is determined to be stable, and the model does not need to be retrained, the system does another check to see whether it should remain in the iterative cycle. Otherwise, if the model is determined to require retraining, the iterated data is used to retrain the analysis model and redeployed at step 525 , before doing another iterative cycle check.
- the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card.
- ASIC application-specific integrated circuit
- Software/hardware hybrid implementations of at least some of the aspects disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory.
- a programmable network-resident machine which should be understood to include intermittently connected network-aware machines
- Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols.
- a general architecture for some of these machines may be described herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented.
- At least some of the features or functionalities of the various aspects disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, or other appropriate computing device), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or other suitable device, or any combination thereof.
- at least some of the features or functionalities of the various aspects disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or other appropriate virtual environments).
- Computing device 10 may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory.
- Computing device 10 may be configured to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
- communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
- computing device 10 includes one or more central processing units (CPU) 12 , one or more interfaces 15 , and one or more busses 14 (such as a peripheral component interconnect (PCI) bus).
- CPU 12 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine.
- a computing device 10 may be configured or designed to function as a server system utilizing CPU 12 , local memory 11 and/or remote memory 16 , and interface(s) 15 .
- CPU 12 may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like.
- CPU 12 may include one or more processors 13 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors.
- processors 13 may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 10 .
- ASICs application-specific integrated circuits
- EEPROMs electrically erasable programmable read-only memories
- FPGAs field-programmable gate arrays
- a local memory 11 such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory
- RAM non-volatile random access memory
- ROM read-only memory
- Memory 11 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. It should be further appreciated that CPU 12 may be one of a variety of system-on-a-chip (SOC) type hardware that may include additional hardware such as memory or graphics processing chips, such as a QUALCOMM SNAPDRAGONTM or SAMSUNG EXYNOSTM CPU as are becoming increasingly common in the art, such as for use in mobile devices or integrated devices.
- SOC system-on-a-chip
- processor is not limited merely to those integrated circuits referred to in the art as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit.
- interfaces 15 are provided as network interface cards (NICs).
- NICs control the sending and receiving of data packets over a computer network; other types of interfaces 15 may for example support other peripherals used with computing device 10 .
- the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like.
- interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRETM, THUNDERBOLTTM, PCI, parallel, radio frequency (RF), BLUETOOTHTM, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like.
- USB universal serial bus
- RF radio frequency
- BLUETOOTHTM near-field communications
- near-field communications e.g., using near-field magnetics
- WiFi wireless FIREWIRETM
- Such interfaces 15 may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity AN hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM).
- an independent processor such as a dedicated audio or video processor, as is common in the art for high-fidelity AN hardware interfaces
- volatile and/or non-volatile memory e.g., RAM
- FIG. 6 illustrates one specific architecture for a computing device 10 for implementing one or more of the aspects described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented.
- architectures having one or any number of processors 13 may be used, and such processors 13 may be present in a single device or distributed among any number of devices.
- a single processor 13 handles communications as well as routing computations, while in other aspects a separate dedicated communications processor may be provided.
- different types of features or functionalities may be implemented in a system according to the aspect that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).
- the system of an aspect may employ one or more memories or memory modules (such as, for example, remote memory block 16 and local memory 11 ) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the aspects described herein (or any combinations of the above).
- Program instructions may control execution of or comprise an operating system and/or one or more applications, for example.
- Memory 16 or memories 11 , 16 may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.
- At least some network device aspects may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein.
- nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory (as is common in mobile devices and integrated systems), solid state drives (SSD) and “hybrid SSD” storage drives that may combine physical components of solid state and hard disk drives in a single hardware device (as are becoming increasingly common in the art with regard to personal computers), memristor memory, random access memory (RAM), and the like.
- ROM read-only memory
- flash memory as is common in mobile devices and integrated systems
- SSD solid state drives
- hybrid SSD hybrid SSD
- such storage means may be integral and non-removable (such as RAM hardware modules that may be soldered onto a motherboard or otherwise integrated into an electronic device), or they may be removable such as swappable flash memory modules (such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices), “hot-swappable” hard disk drives or solid state drives, removable optical storage discs, or other such removable media, and that such integral and removable storage media may be utilized interchangeably.
- swappable flash memory modules such as “thumb drives” or other removable media designed for rapidly exchanging physical storage devices
- hot-swappable hard disk drives or solid state drives
- removable optical storage discs or other such removable media
- program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a JAVATM compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
- interpreter for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language.
- systems may be implemented on a standalone computing system.
- FIG. 7 there is shown a block diagram depicting a typical exemplary architecture of one or more aspects or components thereof on a standalone computing system.
- Computing device 20 includes processors 21 that may run software that carry out one or more functions or applications of aspects, such as for example a client application 24 .
- Processors 21 may carry out computing instructions under control of an operating system 22 such as, for example, a version of MICROSOFT WINDOWSTM operating system, APPLE macOSTM or iOSTM operating systems, some variety of the Linux operating system, ANDROIDTM operating system, or the like.
- an operating system 22 such as, for example, a version of MICROSOFT WINDOWSTM operating system, APPLE macOSTM or iOSTM operating systems, some variety of the Linux operating system, ANDROIDTM operating system, or the like.
- one or more shared services 23 may be operable in system 20 , and may be useful for providing common services to client applications 24 .
- Services 23 may for example be WINDOWSTM services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 21 .
- Input devices 28 may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof.
- Output devices 27 may be of any type suitable for providing output to one or more users, whether remote or local to system 20 , and may include for example one or more screens for visual output, speakers, printers, or any combination thereof.
- Memory 25 may be random-access memory having any structure and architecture known in the art, for use by processors 21 , for example to run software.
- Storage devices 26 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to FIG. 6 ). Examples of storage devices 26 include flash memory, magnetic hard drive, CD-ROM, and/or the like.
- systems may be implemented on a distributed computing network, such as one having any number of clients and/or servers.
- FIG. 8 there is shown a block diagram depicting an exemplary architecture 30 for implementing at least a portion of a system according to one aspect on a distributed computing network.
- any number of clients 33 may be provided.
- Each client 33 may run software for implementing client-side portions of a system; clients may comprise a system 20 such as that illustrated in FIG. 7 .
- any number of servers 32 may be provided for handling requests received from one or more clients 33 .
- Clients 33 and servers 32 may communicate with one another via one or more electronic networks 31 , which may be in various aspects any of the Internet, a wide area network, a mobile telephony network (such as CDMA or GSM cellular networks), a wireless network (such as WiFi, WiMAX, LTE, and so forth), or a local area network (or indeed any network topology known in the art; the aspect does not prefer any one network topology over any other).
- Networks 31 may be implemented using any known network protocols, including for example wired and/or wireless protocols.
- servers 32 may call external services 37 when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services 37 may take place, for example, via one or more networks 31 .
- external services 37 may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in one aspect where client applications 24 are implemented on a smartphone or other electronic device, client applications 24 may obtain information stored in a server system 32 in the cloud or on an external service 37 deployed on one or more of a particular enterprise's or user's premises.
- clients 33 or servers 32 may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 31 .
- one or more databases 34 may be used or referred to by one or more aspects. It should be understood by one having ordinary skill in the art that databases 34 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means.
- one or more databases 34 may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as those referred to in the art as “NoSQL” (for example, HADOOP CASSANDRATM, GOOGLE BIGTABLETM, and so forth).
- SQL structured query language
- variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, or even flat file data repositories may be used according to the aspect. It will be appreciated by one having ordinary skill in the art that any combination of known or future database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular aspect described herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system.
- security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood by one having ordinary skill in the art that any configuration or security subsystems known in the art now or in the future may be used in conjunction with aspects without limitation, unless a specific security 36 or configuration system 35 or approach is specifically required by the description of any specific aspect.
- IT information technology
- FIG. 9 shows an exemplary overview of a computer system 40 as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system 40 without departing from the broader scope of the system and method disclosed herein.
- Central processor unit (CPU) 41 is connected to bus 42 , to which bus is also connected memory 43 , nonvolatile memory 44 , display 47 , input/output (I/O) unit 48 , and network interface card (NIC) 53 .
- I/O unit 48 may, typically, be connected to keyboard 49 , pointing device 50 , hard disk 52 , and real-time clock 51 .
- NIC 53 connects to network 54 , which may be the Internet or a local network, which local network may or may not have connections to the Internet. Also shown as part of system 40 is power supply unit 45 connected, in this example, to a main alternating current (AC) supply 46 . Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein.
- AC alternating current
- functionality for implementing systems or methods of various aspects may be distributed among any number of client and/or server components.
- various software modules may be implemented for performing various functions in connection with the system of any particular aspect, and such modules may be variously implemented to run on server and/or client components.
Abstract
Description
Claims (12)
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